An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

Last update: Nov 09, 2021

Related tags

Deep Learning EquivariantSelfAttention

Overview

EquivariantSelfAttention

An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

This work extends and builds on previous work:

FR Classification using e2cnn, whose code is here.
Attention for FR Classificaion, whose code is here. This work makes extensive use of the e2cnn package: e2cnn.

Imports for General Use

import os
import torch
import e2cnn
import pandas as pd
import numpy as np
import configparser as ConfigParser

import utils

Loading a network:

To load a network import the network files, read in a config file, and load the network with the config file parameters:

from networks import AGRadGalNet, VanillaLeNet, testNet, DNSteerableLeNet, DNSteerableAGRadGalNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

config      = ConfigParser.ConfigParser(allow_no_value=True)
config_path = "configs/CONFIG_NAME"
config.read(config_path)

net = locals()[config['model']['base']](**config['model']).to(device)

Alternatively, if you know which architecture you want to use, only import that and load in the config parameters (i.e. without using locals()):

from networks import DNSteerableAGRadGalNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

config      = ConfigParser.ConfigParser(allow_no_value=True)
config_path = "configs/CONFIG_NAME"
config.read(config_path)

net = DNSteerableAGRadGalNet(**config['model']).to(device)

Loading a model:

To load the best performing model (according to early stopping) from a given model training session:

path_supliment = config['data']['augment']+'/'
model = utils.utils.load_model(config, load_model='best', device=device, path_supliment=path_supliment)

Testing a model:

Follow the example of evaluation.ipynb for model evaluation.

Defining a model:

To define your own model simply create a new config which follows the examples provided in configs.

Training a model:

To train a model use the train.py using your config file:

python3.8 train.py --config YOUR_CONFIG_NAME.cfg >& logs/YOUR_CONFIG_NAME.log

An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.

Related tags

Overview

EquivariantSelfAttention

Imports for General Use

Loading a network:

Loading a model:

Testing a model:

Defining a model:

Training a model:

Owner

AI Virtual Calculator: This is a simple virtual calculator based on Artificial intelligence.

A data-driven maritime port simulator

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

A minimal TPU compatible Jax implementation of NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" https://arxiv.org/abs/2104.02699

Official code for 'Robust Siamese Object Tracking for Unmanned Aerial Manipulator' and offical introduction to UAMT100 benchmark

This repository contains a CBIR system that uses swin transformer to extract image's feature.

Keras-1D-ACGAN-Data-Augmentation

Código de um painel de auto atendimento feito em Python.

Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Simple (but Strong) Baselines for POMDPs

Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado]

Video Autoencoder: self-supervised disentanglement of 3D structure and motion

《K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters》(2020)

Official code repository for ICCV 2021 paper: Gravity-Aware Monocular 3D Human Object Reconstruction

Finding an Unsupervised Image Segmenter in each of your Deep Generative Models

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

🏅 Top 5% in 제2회 연구개발특구 인공지능 경진대회 AI SPARK 챌린지